Homogenization is a technique commonly used in multiscale computational science and engineering for predicting collective response of heterogeneous materials and extracting effective mechanical properties. In this paper, a three-dimensional deep convolutional neural network (3D-CNN) is proposed to predict the effective material properties for representative volume elements (RVEs) with random spherical inclusions. The high-fidelity dataset generated by a computational homogenization approach is used for training the 3D-CNN models. The inference results of the trained networks on unseen data indicate that the network is capable of capturing the microstructural features of RVEs and produces an accurate prediction of effective stiffness and Poisson's ratio. The benefits of the 3D-CNN over conventional finite-element-based homogenization with regard to computational efficiency, uncertainty quantification and model's transferability are discussed in sequence. We find the salient features of the 3D-CNN approach make it a potentially suitable alternative for facilitating material design with fast product design iteration and efficient uncertainty quantification.

中文翻译：

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用于异质材料均质化的三维卷积神经网络 (3D-CNN)

均质化是多尺度计算科学和工程中常用的一种技术，用于预测异质材料的集体响应并提取有效的机械性能。在本文中，提出了一种三维深度卷积神经网络 (3D-CNN) 来预测具有随机球形夹杂物的代表性体积元素 (RVE) 的有效材料特性。通过计算同质化方法生成的高保真数据集用于训练 3D-CNN 模型。训练网络对未知数据的推断结果表明，该网络能够捕捉 RVE 的微观结构特征，并准确预测有效刚度和泊松比。3D-CNN 在计算效率、不确定性量化和模型的可转移性方面优于传统的基于有限元的同质化的优势将依次讨论。我们发现 3D-CNN 方法的显着特征使其成为通过快速产品设计迭代和有效的不确定性量化促进材料设计的潜在合适替代方案。